Results from Minnesota/Wisconsin Automatic Out-of-Service Verification Operational Test

1998 ◽  
Vol 1640 (1) ◽  
pp. 92-99
Author(s):  
Robert L. Smith ◽  
Wen-Jing Huang
Keyword(s):  
Real Time ◽  
Personal Computer ◽  
Potential Benefit ◽  
License Plate ◽  
Commercial Vehicles ◽  
Central Computer ◽  
Operational Test ◽  
New System ◽  
Safety Inspections

A project was designed to enhance the ability of inspectors at fixed safety and weight stations (scales) to identify “out-of-service” (OOS) commercial vehicles and drivers by using advanced video-based license plate scanners linked to database software on a personal computer, the MOOSE system. In Wisconsin the results of safety inspections at the scales are stored in real time on a central computer database. This includes commercial vehicles and drivers placed OOS because of major safety violations. The primary goals were to increase the effectiveness of OOS enforcement efforts, establish a bistate enforcement program, and identify future applications. The technology was tested on a corridor involving three scales in Wisconsin and one in Minnesota on westbound I-90/I-94. The MOOSE system did identify a large number of OOS vehicles and drivers, but upon reinspection, almost no current OOS violations were found. The MOOSE system was successfully implemented at the Minnesota scale, but, as in Wisconsin, very few current OOS violations were identified. Because the Minnesota scale operates 24 hr/day, drivers coming from Wisconsin who are still OOS will probably use bypass routes. The greatest potential benefit from the MOOSE system is likely to be from linking the license plates to a new system that provides safety rating scores. Inspectors could then select vehicles for inspection that have a higher probability of being OOS or having other safety violations.

scholarly journals Quality control in cloth production: a new system for real-time defect detection

2000 ◽  
Author(s):  
Antonio Baldassarre ◽  
Maurizio De Lucia ◽  
Francesca Rossi ◽  
Massimiliano Vannucci
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Defect Detection ◽  
Cloth Production ◽  
New System

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

scholarly journals About Systems of Telecontrol – Biotechnology Control

2018 ◽  
pp. 65-67
Author(s):  
Margarita Postnova ◽  
Aleksey Sklyar
Keyword(s):  
Real Time ◽  
Personal Computer ◽  
Central Office ◽  
The Internet ◽  
Production Processes ◽  
Poultry Houses ◽  
Poultry Farms ◽  
Control And Management ◽  
And Control ◽  
Positive Results

Currently, powerful modern poultry farms require built-in logistics with an optimized structure of control and management. Such a system requires formalization and ranking, responding to the tasks of specific divisions of enterprises and poultry farms in general. The analysis of the robots on the Russko-Vysotskaya site shows positive results when the Big Dutchman company introduced the BigFarmNet Manager and AMAKS systems for 11 years of operation of this complex allowing to regulate and control the production processes of egg processing, feeding, drinking, to manage the microclimate of poultry houses in real time from the central office or from a portable personal computer using the Internet.

NEAR REAL TIME PHOTOGRAMMETRY ON A PERSONAL COMPUTER

2006 ◽  
Vol 13 (75) ◽  
pp. 415-422 ◽  
Author(s):  
H. Rüther ◽  
N. Parkyn
Keyword(s):  
Real Time ◽  
Personal Computer
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